research-article

SCANet: Sensor-based Continuous Authentication with Two-stream Convolutional Neural Networks

Authors:

Gang ZhouAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 16, Issue 3

Article No.: 29, Pages 1 - 27

https://doi.org/10.1145/3397179

Published: 21 July 2020 Publication History

Abstract

Continuous authentication monitors the security of a system throughout the login session on mobile devices. In this article, we present SCANet, a two-stream convolutional neural network--based continuous authentication system that leverages the accelerometer and gyroscope on smartphones to monitor users’ behavioral patterns. We are among the first to use two streams of data—frequency domain data and temporal difference domain data—from the two sensors as the inputs of the convolutional neural network (CNN). SCANet utilizes the two-stream CNN to learn and extract representative features and then performs the principal component analysis to select the top 25 features with high discriminability. With the CNN-extracted features, SCANet exploits the one-class support vector machine to train the classifier in the enrollment phase. Based on the trained CNN and classifier, SCANet identifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the effectiveness of the two-stream CNN and the performance of SCANet on our dataset and BrainRun dataset, and the experimental results demonstrate that CNN achieves 90.04% accuracy, and SCANet reaches an average of 5.14% equal error rate on two datasets and takes approximately 3 s for user authentication.

References

[1]

Arsalan Mosenia, Susmita Sur-Kolay, Anand Reghunathan, and Niraj K. Jha. 2017. CABA: Continuous authentication based on BioAura. IEEE Trans. Comput. 66, 5 (2017), 759--772.

Digital Library

[2]

Pin Shen Teh, Andrew Beng Jin Teoh, and Shigang Yue. 2013. A survey of keystroke dynamics biometrics. Sci. World J. (2013), Article ID 408280, 24 pages.

[3]

Koichiro Niinuma, Unsang Park, and Anil K. Jain. 2010. Soft biometric traits for continuous user authentication. IEEE Trans. Inf. Forens. Secur. 5, 4 (2010), 771--780.

Digital Library

[4]

C. Sanchez-Avila and R. Sanchez-Reillo. 2005. Two different approaches for iris recognition using gabor filters and multiscale zero-crossing representation. Pattern Recogn. 38, 2 (2005), 231--240.

[5]

Lin Hong and Anil Jain. 1998. Integrating faces and fingerprints for personal identification. IEEE Trans. Pattern Anal. Mach. Intell. 20, 12 (1998), 1295--1307.

Digital Library

[6]

Ivan Martinovic, Kasper Rasmussen, Marc Roeschlin, and Gene Tsudik. 2017. Authentication using pulse-response biometrics. Commun. ACM 60, 2 (2017), 108--115.

Digital Library

[7]

Frederic Bimbot, Jean-Francois Bonastre, Corinne Fredouille, et. al. 2004. A tutorial on text-independent speaker verification. EURASIP J. Adv. Sign. Process. 2004, 4 (2004), 430--451.

Digital Library

[8]

Huan Feng, Kassem Fawaz, and Kang G. Shin. 2017. Continuous authentication for voice assistants. In Proceedings of the 23rd ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’17). ACM/IEEE, 343--355.

[9]

Shanxun Chen, Amit Pande, and Prasant Mohapatra. 2014. Sensor-assisted facial recognition: An enhanced biometric authentication system for smartphones. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’14). ACM, 109--122.

Digital Library

[10]

Manuele Bicego, Andrea Lagorio, Enrico Grosso, and Massimo Tistarelli. 2006. On the use of sift features for face authentication. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06). IEEE, 1--7.

Digital Library

[11]

Cheng Bo, Lan Zhang, Taeho Jung, Junze Han, Xiang-Yang Li, and Yu Wang. 2014. Continuous user identification via touch and movement behavioral biometrics. In Proceedings of IEEE 33rd International Performance Computing and Communications Conference (IPCCC’14). IEEE, 1--8.

[12]

Bin Zou and Yantao Li. 2018. Touch-based smartphone authentication using import vector domain description. In Proceedings of the 29th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP’18). IEEE, 85--88.

[13]

J. Mantyjarvi, M. Lindholm, E. Vildjiounaite, S.-M. Makela, and H. Ailisto. 2005. Identifying users of portable devices from gait pattern with accelerometers. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’05). IEEE, II973-II976.

[14]

Yantao Li, Hailong Hu, Gang Zhou, and Shaojiang Deng. 2018. Sensor-based continuous authentication using cost-effective kernel ridge regression. IEEE Access 6, 5 (2018), 32554--32565.

[15]

Senaka Buthpitiya, Ying Zhang, Anind K. Dey, and Martin Griss. 2011. n-gram geo-trace modeling. In Proceedings of the International Conference on Pervasive Computing, Lecture Notes in Computer Science. 97--114.

[16]

Ge Peng, Gang Zhou, David T. Nguyen, Xin Qi, Qing Yang, and Shuangquan Wang. 2017. Continuous authentication with touch behavioral biometrics and voice on wearable glasses. IEEE Trans. Hum.-Mach. Syst. 47, 3 (2017), 404--416.

[17]

Mario Parreño Centeno, Yu Guan, and Ada van Moorsel. 2018. Mobile based continuous authentication using deep features. In Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning (EMDL’18). IEEE, 19--24.

Digital Library

[18]

Mario Parreño Centeno, Ada van Moorsel and Stefano Castruccio. 2017. Smartphone continuous authentication using deep learning autoencoders. In Proceedings of 15th Annual Conference on Privacy, Security and Trust (PST’17). IEEE, 147--155.

[19]

Chris Xiaoxuan Lu, Bowen Du, Peijun Zhao, Hongkai Wen, Yiran Shen, Andrew Markham, and Niki Trigoni. 2018. DeepAuth: In-situ authentication for smartwatches via deeply learned behavioural biometrics. In Proceedings of the International Symposium on Wearable Computers (ISWC’18). ACM, 204--207.

Digital Library

[20]

Upal Mahbub, Vishal M. Patel, Deepak Chandra, Brandon Barbello, and Rama Chellappa. 2016. Partial face detection for continuous authentication. In Proceedings of the IEEE International Conference on Image Processing (ICIP’16). IEEE, 2991--2995.

[21]

A. Hadid, J. Y. Heikkila, O. Silven, and M. Pietikainen. 2007. Face and eye detection for person authentication in mobile phones. In Proceedings of the ACM/IEEE International Conference on Distributed Smart Cameras. ACM/IEEE, 101--108.

[22]

Aditi Roy, Tzipora Halevi, and Nasir Memon. 2015. An HMM-based multi-sensor approach for continuous mobile authentication. In Proceedings of the 2015 IEEE Military Communications Conference (MILCOM’15). 1311--1316.

[23]

Zden̆ka Sitová, Jaroslav S̆edĕnka, Qing Yang, Ge Peng, Gang Zhou, Paolo Gasti, and Kiran S. Balagani. 2016. Hmog: New behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forens. Secur. 11, 5 (2016), 877--892.

Digital Library

[24]

Nabilah Shabrina, Tsuyoshi Isshiki, and Hiroaki Kunieda. 2016. Fingerprint authentication on touch sensor using phase-only correlation method. In Proceedings of the 7th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES’16). IEEE, 85--89.

[25]

Chao Shen, Yuanxun Li, Yufei Chen, Xiaohong Guan, and Roy A. Maxion. 2018. Performance analysis of multi-motion sensor behavior for active smartphone authentication. IEEE Trans. Inf. Forens. Secur. 13, 1 (2018), 48--62.

[26]

Yantao Li, Hailong Hu, and Gang Zhou. 2019. Using data augmentation in continuous authentication on smartphones. IEEE IoT J. 6, 1 (2019), 628--640.

[27]

Oren Rippel, Jasper Snoek, and Ryan P. Adams. 2015. Spectral representations for convolutional neural networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15). ACM, 2449--2457.

[28]

Tao Feng, Jun Yang, Zhixian Yan, Emmauenl M. Tapia, and Weidong Shi. 2014. Tips: Contextaware implicit user identification using touch screen in uncontrolled environments. In Proceedings of the 15th International Workshop on Mobile Computing Systems and Applications (HotMobile’14). ACM, 1--6, 2014.

Digital Library

[29]

Hailong Hu, Yantao Li, Zhangqian Zhu, and Gang Zhou. 2018. CNNAuth: Continuous authentication via two-stream convolutional neural networks. In Proceedings of 2018 IEEE 13th International Conference on Networking, Architecture and Storage (NAS’18). IEEE, 1--9.

[30]

Tim Cooijmans, Nicolas Ballas, Cesar Laurent, caglar Gulcehre and Aaron Courville. 2017. Recurrent batch normalization. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17). 1--13.

[31]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE, 770--778.

[32]

Young-Jin Cha, Wooram Choi, Gahyun Suh, Sadegh Mahmoudkhani, and Oral Büyüköztürk. 2018. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput.-Aid. Civil Infrastr. Eng. 33, 9 (2018), 731--747.

Digital Library

[33]

Young-Jin Cha, Wooram Choi, and Oral Büyüköztürk. 2017. Deep learning-based crack damage detectionu sing convolutional neural networks. Comput.-Aid. Civil Infrastr. Eng. 32, 5 (2017) 361--378.

Digital Library

[34]

Alex Krizhevsky, Ilya Sutskever, and Geofrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12). ACM, 1097--1105.

Digital Library

[35]

Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of International Conference on Learning Representations (ICLR’15). 1--14.

[36]

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv Preprint arXiv:1704.04861 (2017).

[37]

Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2017. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’18). IEEE, 6848--6856.

[38]

Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). IEEE, 4510--4520.

[39]

Karen Simonyan and Andrew Zisserman. 2014. Two-stream convolutional networks for action recognition in videos. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14). ACM, 568--576.

[40]

Laurent Slfre and Stephane Mallat. 2014. Rigid-motion scattering for texture classification. Int. J. Comput. Vis. 107, 2 (2014), 501--515.

[41]

Francois Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 1800--1807.

[42]

Lingjun Li, Xinxin Zhao, and Guoliang Xue. 2013. Unobservable re-authentication for smartphones. In Proceedings of the 20th Annual Network and Distributed System Security Symposium (NDSS’13). 1--16.

[43]

Cheng Bo, Lan Zhang, Xiang-Yang Li, Qiuyuan Huang, and Yu Wang. 2013. SilentSense: Silent user identification via touch and movement behavioral biometrics. In Proceedings of the 19th Annual International Conference on Mobile Computing and Networking (MobiCom’13). ACM, 187--190.

Digital Library

[44]

Larry M. Manevitz and Malik Yousef. 2002. One-class svms for document classification. J. Mach. Learn. Res. 2, 2 (2002), 139--154.

Digital Library

[45]

Yantao Li, Bin Zou, Shaojiang Deng, and Gang Zhou. 2020. Using feature fusion strategies in continuous authentication on smartphones. IEEE Internet Comput. 24, 2 (2020), 49--56.

[46]

Wei-Han Lee and Ruby Lee. 2016. Implicit sensor-based authentication of smartphone users with smartwatch. In Proceedings of the Hardware and Architectural Support for Security and Privacy (HASP’16). 1--8.

Digital Library

[47]

Senaka Buthpitiya, Ying Zhang, Anind K. Dey, and Martin L. Griss. 2011. n-gram geo-trace modeling. In Proceedings of International Conference on Pervasive Computing (ICPC’11). IEEE, 97--114.

[48]

Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. 2017. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 351--360.

Digital Library

[49]

Matteo Gadaleta and Michele Rossi. 2018. IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recogn. 74, 2 (2018), 25--37.

Digital Library

[50]

Chao Shen, Tianwen Yu, Sheng Yuan, Yunpeng Li, and Xiaohong Guan. 2016. Performance analysis of motion-sensor behavior for user authentication on smartphones. Sensors 16, 3 (2016), 345:1--21.

[51]

Leif E. Peterson. 2009. K-nearest neighbor. Scholarpedia 4, 2 (2009), 1883.

[52]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference for Learning Representations (ICLR’15). 1--15.

[53]

Alexander Senf, Xue-wen Chen, and Anne Zhang. 2006. Comparison of one-class SVM and two-class SVM for fold recognition. In Proceedings of 2006 International Conference on Neural Information Processing (ICONIP’06). Lecture Notes in Computer Science, 140--149.

Digital Library

[54]

Pei-Yuan Wu, Chi-Chen Fang, Jien Morris Chang, and Sun-Yuan Kung. 2016. Cost-effective kernel ridge regression implementation for keystroke-based active authentication system. IEEE Trans. Cybernet. 47, 11 (2017), 3916--3927.

[55]

Michail D. Papamichail, Kyriakos C. Chatzidimitriou, Thomas Karanikiotis, Napoleon-Christos I. Oikonomou, Andreas L. Symeonidis, and Sashi K. Saripalle. 2019. BrainRun: A behavioral biometrics dataset towards continuous implicit authentication. Data 4, 2 (2019), 1--17.

Cited By

Yoon JPark JKim JTeoh A(2024)CoreTemp: Coreset Sampled Templates for Multimodal Mobile BiometricsApplied Sciences10.3390/app1412518314:12(5183)Online publication date: 14-Jun-2024
https://doi.org/10.3390/app14125183
Davydenko SKostyuchenko ENovikov S(2024)Evaluation of the Informativeness of Features in Datasets for Continuous VerificationОценивание информативности признаков в наборах данных для проведения продлённой аутентификацииInformatics and AutomationИнформатика и автоматизация10.15622/ia.23.1.323:1(65-100)Online publication date: 11-Jan-2024
https://doi.org/10.15622/ia.23.1.3
Sheng BLi JGui LGuo ZXiao F(2024)LiteWiSys: A Lightweight System for WiFi-based Dual-task Action PerceptionACM Transactions on Sensor Networks10.1145/363217720:4(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3632177
Show More Cited By

Index Terms

SCANet: Sensor-based Continuous Authentication with Two-stream Convolutional Neural Networks

Recommendations

Mobile Based Continuous Authentication Using Deep Features
EMDL'18: Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning

Continuous authentication is a promising approach to validate the user's identity during a work session, e.g., for mobile banking applications. Recently, it has been demonstrated that changes in the motion patterns of the user may help to note the ...
AuthConFormer: Sensor-based Continuous Authentication of Smartphone Users Using A Convolutional Transformer
Abstract
To prevent the leakage of personal information on smartphones, a method that can authenticate the smartphone user’s identity throughout the session is essential. Sensor-based continuous authentication is a promising potential approach that uses ...
Pulse-Response: Exploring Human Body Impedance for Biometric Recognition

Biometric characteristics are often used as a supplementary component in user authentication and identification schemes. Many biometric traits, both physiological and behavioral, offering a wider range of security and stability, have been explored. We ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 16, Issue 3

August 2020

263 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3399417

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 21 July 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 February 2020

Received: 01 February 2019

Published in TOSN Volume 16, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

63
Total Citations
View Citations
550
Total Downloads

Downloads (Last 12 months)78
Downloads (Last 6 weeks)5

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yoon JPark JKim JTeoh A(2024)CoreTemp: Coreset Sampled Templates for Multimodal Mobile BiometricsApplied Sciences10.3390/app1412518314:12(5183)Online publication date: 14-Jun-2024
https://doi.org/10.3390/app14125183
Davydenko SKostyuchenko ENovikov S(2024)Evaluation of the Informativeness of Features in Datasets for Continuous VerificationОценивание информативности признаков в наборах данных для проведения продлённой аутентификацииInformatics and AutomationИнформатика и автоматизация10.15622/ia.23.1.323:1(65-100)Online publication date: 11-Jan-2024
https://doi.org/10.15622/ia.23.1.3
Sheng BLi JGui LGuo ZXiao F(2024)LiteWiSys: A Lightweight System for WiFi-based Dual-task Action PerceptionACM Transactions on Sensor Networks10.1145/363217720:4(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3632177
Li YLiu LDeng SQin HEl-Yacoubi MZhou G(2024)Memory-Augmented Autoencoder based Continuous Authentication on Smartphones with Conditional Transformer GANsIEEE Transactions on Mobile Computing10.1109/TMC.2023.3290834(1-16)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3290834
Li YSun XYang ZHuang H(2024)SNNAuth: Sensor-Based Continuous Authentication on Smartphones Using Spiking Neural NetworksIEEE Internet of Things Journal10.1109/JIOT.2024.334953311:9(15957-15968)Online publication date: 1-May-2024
https://doi.org/10.1109/JIOT.2024.3349533
Zhao CGao FShen Z(2024)AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality DataIEEE Internet of Things Journal10.1109/JIOT.2023.331471711:4(6928-6942)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3314717
Shen ZLi SZhao XZou J(2024)IncreAuth: Incremental-Learning-Based Behavioral Biometric Authentication on SmartphonesIEEE Internet of Things Journal10.1109/JIOT.2023.328993511:1(1589-1603)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3289935
Wagata KTeoh A(2024)Cross-Domain Cross-Task Transfer Mobile Touch-Stroke AuthenticationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10445813(4445-4449)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10445813
Uslu Uİncel ÖAlptekin G(2024)Evaluation of Deep Learning Models for Continuous Authentication Using Behavioral BiometricsProcedia Computer Science10.1016/j.procs.2023.10.115225:C(1272-1281)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.115
Alawami MAbuhmed TAbuhamad MKim H(2024)MotionID: Towards practical behavioral biometrics-based implicit user authentication on smartphonesPervasive and Mobile Computing10.1016/j.pmcj.2024.101922101(101922)Online publication date: Jul-2024
https://doi.org/10.1016/j.pmcj.2024.101922
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents